@Article{csse.2023.030627,
AUTHOR = {Kavitha Muthukumaran, K. Hariharanath, Vani Haridasan},
TITLE = {Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {45},
YEAR = {2023},
NUMBER = {1},
PAGES = {887--901},
URL = {http://www.techscience.com/csse/v45n1/49337},
ISSN = {},
ABSTRACT = {Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets. This paper presents a novel multi-<i>vs</i>. optimization (MVO) based feature selection (FS) with an optimal variational auto encoder (OVAE) model for FCP. The proposed multi-<i>vs</i>. optimization based feature selection with optimal variational auto encoder (MVOFS-OVAE) model mainly aims to accomplish forecasting the financial crisis. For achieving this, the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization. In addition, the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach. Followed by, the variational auto encoder (VAE) model is applied for the categorization of financial data into financial crisis or non-financial crisis. Finally, the differential evolution (DE) algorithm is utilized for the parameter tuning of the VAE model. A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.},
DOI = {10.32604/csse.2023.030627}
}